Quickstart demo

Outline

1. Make a data folder

2. Drag favorability.csv into the data folder

3. Make existing folder and RStudio project

4. Open an R Markdown Notebook

5. library(tidyverses) plus other libraries

6. IMPORT data See Also RStudio data import wizard

7. ATTACHE data

8. EDA: Visualize ggplot(data = starwars, aes(hair_color)) + geom_bar()

9. EDA: skimr::skim(starwars)

10. EDA: summary(fav_rating)

11. left_join(starwars, fivethirtyeight)

12. Transform data: five dplyr verbs …

    • count / group_by & summarize

13. Interactive visualization ggplotly

14. Quick Linear Regression

15. Reports: notebooks, slides, dashboards, word document, PDF, book, etc.

---

5. library(tidyverses) plus other libraries

library(tidyverse)
library(skimr)
library(plotly)
library(moderndive)
library(broom)

6. read_csv(file_name.csv)

See Also data import wizard

## fav_data <- read_csv("data/fav.csv")
favorability <- read_csv("https://raw.githubusercontent.com/libjohn/intro2r-code/master/data/538_favorability_popularity.csv", skip = 11)

7 attached on-board data

• dplyr::starwars

dplyr::starwars

data("starwars")

8 Quick visualization

Visualize with the ggplot2 library.

plot <- ggplot(data = starwars, 
               aes(x = hair_color)) + 
  geom_bar()
plot

One improvement

Arrange bars by frequency using forcats::fct_infreq()

plot1 <- ggplot(starwars, 
                aes(fct_infreq(hair_color))) + 
  geom_bar()
plot1

9. skimr::skim(starwars)

The skimr library presents summary EDA results using the skim() function

skim(starwars)

-- Data Summary ------------------------
                           Values  
Name                       starwars
Number of rows             87      
Number of columns          14      
_______________________            
Column type frequency:             
  character                8       
  list                     3       
  numeric                  3       
________________________           
Group variables            None    

-- Variable type: character --------------------------------------------------------------------------------
# A tibble: 8 x 8
  skim_variable n_missing complete_rate   min   max empty n_unique whitespace
* <chr>             <int>         <dbl> <int> <int> <int>    <int>      <int>
1 name                  0         1         3    21     0       87          0
2 hair_color            5         0.943     4    13     0       12          0
3 skin_color            0         1         3    19     0       31          0
4 eye_color             0         1         3    13     0       15          0
5 sex                   4         0.954     4    14     0        4          0
6 gender                4         0.954     8     9     0        2          0
7 homeworld            10         0.885     4    14     0       48          0
8 species               4         0.954     3    14     0       37          0

-- Variable type: list -------------------------------------------------------------------------------------
# A tibble: 3 x 6
  skim_variable n_missing complete_rate n_unique min_length max_length
* <chr>             <int>         <dbl>    <int>      <int>      <int>
1 films                 0             1       24          1          7
2 vehicles              0             1       11          0          2
3 starships             0             1       17          0          5

-- Variable type: numeric ----------------------------------------------------------------------------------
# A tibble: 3 x 11
  skim_variable n_missing complete_rate  mean    sd    p0   p25   p50   p75  p100 hist 
* <chr>             <int>         <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 height                6         0.931 174.   34.8    66 167     180 191     264 ▁▁▇▅▁
2 mass                 28         0.678  97.3 169.     15  55.6    79  84.5  1358 ▇▁▁▁▁
3 birth_year           44         0.494  87.6 155.      8  35      52  72     896 ▇▁▁▁▁

10. summary

summary(favorability)

     name             fav_rating   
 Length:14          Min.   :110.0  
 Class :character   1st Qu.:148.5  
 Mode  :character   Median :392.0  
                    Mean   :369.0  
                    3rd Qu.:559.5  
                    Max.   :610.0  

11. left_join(starwars, fivethirtyeight)

Joins or merges are part of thedplyr library.

starwars %>% 
  left_join(favorability, by = "name") %>% 
  select(name, fav_rating, everything()) %>% 
  arrange(-fav_rating)

12. Transform data:

From the dplyr library, use the five verbs …

select to subset data by columns

starwars %>% 
  select(name, gender, hair_color)

filter to subset data rows

starwars %>% 
  filter(gender == "feminine")

arrange to sort data

starwars %>% 
  arrange(desc(height), desc(name))

mutate to add new variable or transform existing

starwars %>%
  drop_na(mass) %>% 
  select(name, mass) %>% 
  mutate(big_mass = mass * 2)

count / group_by & summarize

subtotals of variables

starwars %>% 
  count(gender)

Variable totals (and also, but not here, calculations)

starwars %>% 
  drop_na(mass) %>% 
  summarise(sum(mass))

Variable subtotals and calculations

group_by(gender, species) %>% summarise(mean_height = mean(height), total = n())

starwars %>% 
  drop_na(height) %>% 
  group_by(gender, species) %>% 
  summarise(mean_height = mean(height), total = n()) %>% 
  arrange(desc(total)) %>%
  drop_na(species) %>%
  filter(total > 1) %>% 
  select(species, gender, total, everything())

13. Interactive visualization

from the plotly library

ggplotly(plot1)

14. Regression / models

Predict mass from height after eliminating Jabba from the data set. Here we’ll use primarily base R, moderndive for model outputs, and tidyverse for the pipe %>% and dplyr for data transformations. Plus, alternatively, the broom library to manipulate models.

model <- lm(mass ~ height, data = starwars %>% filter(mass < 500))
model

Call:
lm(formula = mass ~ height, data = starwars %>% filter(mass < 
    500))

Coefficients:
(Intercept)       height  
   -32.5408       0.6214  

summary(model)

Call:
lm(formula = mass ~ height, data = starwars %>% filter(mass < 
    500))

Residuals:
    Min      1Q  Median      3Q     Max 
-39.382  -8.212   0.211   3.846  57.327 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -32.54076   12.56053  -2.591   0.0122 *  
height        0.62136    0.07073   8.785 4.02e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 19.14 on 56 degrees of freedom
Multiple R-squared:  0.5795,    Adjusted R-squared:  0.572 
F-statistic: 77.18 on 1 and 56 DF,  p-value: 4.018e-12

A nice Explanation of Basic Regression can be found in chapter 5 of the book Statistical Inference via Data Science. You can also use the moderndive library packages to access helpful functions such as: get_correlatin(), get_regression_table(), etc.

You may also appreciate or prefer the broom package for the very nice tidy(), glance(), and augment() functions.

starwars %>% 
  filter(mass < 500) %>% 
  get_correlation(mass ~ height)

# tidy(model)
get_regression_table(model)

# broom::glance(model)
get_regression_summaries(model)

# broom::augment(model)
get_regression_points(model)

Visualize regression

mass over height with a fitted linear regression line and confidence interval using geom_smooth()

starwars %>% 
  filter(mass < 500) %>%
  ggplot(aes(height, mass)) +
  geom_jitter() +
  geom_smooth(method = "lm")

15. Render reports

By changing the argument in the YAML header, you can render many report styles. A few popular examples include

type	YAML syntax	More information
notebook (alpha or dev)	output: html_notebook	Notebook
notebook (final or prod)	output: html_document	HTML document
Word document	output: word_document	MS Word
slide deck	See Get Started	Xaringan
dashboards		flexdashboard or shiny
e-book / web-book		Bookdown
website		Blogdown
website (simple)		Create a website / Distill
PDF	output: pdf_document	PDF document